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Chronic insufficient and poor-quality sleep are linked to hypertension, diabetes, depression, heart attack, and stroke. While studies on substance use and sleep typically focus on people in or entering treatment, there is a lack of research on sleep health among community-recruited people who inject drugs (PWID). To address this literature gap, we examined factors associated with insufficient and poor-quality sleep among community-recruited PWID. We recruited and interviewed 472 active opioid-using PWID (injected within the last 30 days) in Los Angeles, CA and Denver, CO between 2021 and 2022. Participants completed computer-assisted interviews covering demographics, subsistence measures, drug use patterns, injection-related behaviors, health risks, and sleep duration and quality in the last 3 months. Descriptive statistics were used to analyze all variables for subjects with complete responses to sleep items (n = 464). Bivariate analyses determined factors associated with sleep measures using chi-square and t-tests. Collinear variables were removed, and binomial linear multivariable regression calculated risk ratios (RR) for insufficient and poor-quality sleep in the last 3 months. Participants exhibited low sleep duration (mean = 4.99, standard deviation (SD) = 2.70), with 76% reporting insufficient sleep and 62% poor-quality sleep. Bivariate analyses associated both sleep measures with drug use, high subsistence scores, violent victimization, and poor health outcomes. Multivariable analyses showed a high subsistence score predicting insufficient (RR = 1.31) and poor-quality sleep (RR = 1.69) compared to low subsistence. Poor sleep health is common among structurally vulnerable community-recruited PWID, as measured by subsistence index associated with adverse sleep outcomes. Further research on structural interventions to address sleep and subsequent health outcomes among PWID is imperative. [ABSTRACT FROM AUTHOR]

Coupling-onto approach was equipped to synthesis seven AB2-type miktoarm stars nanomaterials using carbodiimide chemistry. Glycine, the simplest amino acid is chosen as “arm A”, seven biodegradable amides and polyamides as various “arm B” and amino adipic acid as “core” were used. The structures of these hydrophobic tridentate nano-stars were confirmed with FT-IR and 1H NMR. The diameter of the nano-stars recorded via DLS ranged from 256.3 nm to 933.4 nm. TGA analysis of the product reported a noticeable loss of 32% by total mass at 400 °C, indicating thermal degradation. The products show significant structural degradation after 15 days of biological treatment with E. coli. All the seven tridentate nano-stars can potentially be equipped as drug-delivery vehicles. [ABSTRACT FROM AUTHOR]

Coffee leaves are currently emerging as a promising agri-food resource rich in phenolics. This study aims to valorise coffee leaf powder (CLP) by incorporating it in refined wheat flour rusk formulations and analyse its physio-chemical, rheological, functional and sensory characteristics. The progressive replacement of CLP improved the dietary fibre (2.51 ± 0.2%), ash (1.09 ± 0.11%), and water absorption capacity (59.7 ± 0.1%) of the flours. It considerably enhanced the falling number and sedimentation values of the flour blends while decreased the loaf volume. Progressive increase in the dietary phenolics (232.21–435.19 mg/100 g), chlorogenic acid (6.0–7.5 mg/100 g), and ABTS antioxidant activity (963.89–1607.25 µMTEAC/g) of the rusks was observed upon CLP addition. Rusks with 3% CLP were found to have significantly acceptable physical and sensory characteristics. Thus, supplementation of CLP in rusk helps in valorising coffee leaves besides providing a functional bakery product to the coffee industry. [ABSTRACT FROM AUTHOR]

In present research, failure analysis of industrial screw conveyors has been carried out. For this work, failure data of twenty-three similar screw conveyors are gathered from same plant. Failure causes and effects in screw shaft, flights, bearings, bushes, couplings and troughs of conveyor are identified. From the effects observed, the initial trouble shooting and fault diagnosis of conveyor can easily be carried out by the operator. Failure mode and effect analysis of the gathered data has been carried out for all the conveyor components. Major components contributing to failure of conveyor assembly are bearings and bushes. Scanning electron microscopy of the failed bush and bearing has been carried out at four different magnification levels. From the scanning electron microscopy, the pits on the surfaces are clearly observed. These pits are due to inclusion for foreign and rust particles in the assembly. Rusting is identified as the root cause of failure which further leads to pitting. Also, energy-dispersive spectroscopy (EDS) of the bearing and bush has been performed. From this spectroscopy, the presence of oxygen, iron and moisture has been identified on the damaged surfaces. Implementable preventive measures, to avoid failure of all the components of screw conveyor, are suggested. [ABSTRACT FROM AUTHOR]

In this paper, we study the arithmetic nature of series of the form ∑ ω ∈ Λ A (ω) B (ω) , where Λ is a two-dimensional lattice in C , A(X) and B(X) are suitable polynomials over C , with deg A ≤ deg B - 3 . In particular, we focus on the cases when the roots of the polynomial B(X) are either algebraic numbers or rational multiples of a non-zero period of Λ . [ABSTRACT FROM AUTHOR]

Purpose: Mesoporous silica-based formulations of poorly soluble drugs may exhibit incomplete drug release due to drug remaining adsorbed on the silica surface. The goal of this study was (1) to evaluate the adsorption tendency of atazanavir from aqueous solution onto mesoporous silica (SBA-15) and (2) to determine if the drug release from mesoporous silica formulations was promoted by the presence of an absorptive compartment during dissolution testing. Methods: Atazanavir (ATZ) formulations with different drug loadings were prepared by incipient impregnation. The solid-state properties of the formulations were analyzed by X-ray diffraction (XRD), differential scanning calorimetry (DSC), infrared spectroscopy and thermogravimetric analysis. Drug release was compared for closed compartment versus absorptive dissolution testing at gastric and intestinal pH. Results: XRD and DSC showed that all formulations were amorphous. Infrared spectra indicated intermolecular interactions between silanol groups in SBA-15 and carbonyl groups in atazanavir. Nanoconfinement of drug in silica mesopores was suggested by thermal analysis. Closed compartment dissolution testing showed incomplete drug release, largely due to the adsorption tendency of ATZ. However, coupled dissolution-absorption studies showed complete release over a 240 min time period. This suggested that the depletion of drug in the dissolution medium due to drug diffusion across the membrane promotes drug release. Drug release was further improved when the formulation was first added to fasted state gastric pH conditions followed by pH-shift to intestinal conditions, which was attributed to the higher solubility of atazanavir at low pH. However, ATZ mesoporous silica formulations showed a poorer overall absorption behavior relative to a polymer-based amorphous solid dispersion formulation. Conclusion: This study highlights that absorptive dissolution conditions promote drug desorption from the silica surface and hence, enhance drug release. Further, the influence of solution pH on drug release underscores the need to consider how variations in physiological conditions may impact the performance of mesoporous silica-based formulations. [ABSTRACT FROM AUTHOR]

A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide images from the largest reported cohort to date. The model showed promising performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification with validation on an external test dataset, suggesting potential for AI-assisted neuroblastoma classification., (© 2024. The Author(s).)

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology., (© 2023. The Author(s).)

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability., (© 2023. The Author(s).)